System and Method for Processing Image for Identifying Alphanumeric Characters Present in a Series

ABSTRACT

A system and a method for identification of alphanumeric characters present in a series in an image are disclosed. The system and method captures the image and further processes it for binarization by computing a pattern of the image. The generated binarized images are then filtered for removing unwanted components. Candidate images are identified out of the filtered binarized images. All the obtained candidate images are combined to generate a final candidate image which is further segmented in order to recognize a valid alphanumeric character present in the series.

FIELD OF THE INVENTION

The present invention in general relates to method and system forcharacter identification. More particularly, the invention relates to amethod and system for identifying alphanumeric characters present in aseries in an image.

BACKGROUND OF THE INVENTION

The images of the Vehicle Identification Number (VIN) are captured bythe mobile phone camera by common people many of the times for somespecific purpose in some extraordinary situations. Manual involvement inthe capturing process, uneven and insufficient illumination, andunavailability of sophisticated focusing system yield poor qualityimages.

The performance of available open source Optical Character Recognition(OCR) systems on VIN images captured by mobile phones is extremely poorbecause of the image quality affected by various noises. Therefore,image enhancement techniques need to be used before giving a scannedimage as an input to Optical character recognition system. Binarizationtechnique is used as an image enhancement technique to get the textregion from a complex background, more specifically the backgroundtexts.

The OCR for texts in mobile camera captured images consists of a varietyof shortcomings. In the existing system, it is required to extractindividual characters on embedded mobile platform which has low memoryand processing speed. Binarization technique is used as an imageenhancement technique to get the text region from a complex background,more specifically the background texts. Many Binarization techniqueshave been proposed to improve the recognition accuracy of the images.The existing Binarization techniques can improve the recognitionaccuracy of the images only up to 5.89% at most.

Therefore, there is a need of a system and method capable of providing asuitable low complexity binarization technique which would improve therecognition accuracy of an image to a greater extent.

OBJECTS OF THE INVENTION

It is the primary object of the invention to provide a system and methodfor identification of alphanumeric characters present in a series in animage.

It is another object of the invention to provide a system and method forperforming binarization of the image thus captured.

It is yet another embodiment of the invention to provide a system andmethod for removing unwanted, over-segmented and under-segmentedsegments from the binarized images.

It is yet another object of the invention to provide a system and methodfor applying morphological closing for merging the multiple componentlabels in the valid alphanumeric characters.

SUMMARY OF THE INVENTION

The present invention provides a method for identification ofalphanumeric characters present in a series in an image. The methodcomprises of processor implemented steps of capturing the imagecomprising the series of alphanumeric characters and processing theimage for producing a set of identifiable characters out of the seriesof alphanumeric characters. The processing further comprises ofcomputing a pattern for recognizing a pixel intensity distribution inthe image for determining a background peak and a foreground peak,generating a plurality of binarized images by selecting a plurality ofdynamic threshold values between the background peak and the foregroundpeak and filtering the generated binarized images by removing unwantedcomponents from plurality of images to identify one or more validcharacters. The processing further comprises of identifying one or morecandidate images by comparing the valid characters with respect to aknown ground truth value, generating a final candidate image bycombining the candidate images such that the combination of thecandidate images is dependent upon a predefined condition and splittingthe final candidate image into a predefined segments and recognizing avalid alphanumeric character associated with each segment therein.

The present invention also provides a system for identification ofalphanumeric characters present in a series in an image. The systemcomprises of an image capturing device for capturing the imagecomprising the alphanumeric characters present in the series and aprocessor configured to produce a set of identifiable characters out ofthe series of alphanumeric characters. The processor further comprisesof a computing module configured to compute a pattern for recognizing apixel intensity distribution in the image for determining a backgroundpeak and a foreground peak, a binarization module configured to generatea plurality of binarized images by selecting a plurality of dynamicthreshold values between the background peak and the foreground peak anda filter configured to remove unwanted components from the plurality ofimages to identify one or more valid characters. The processor furthercomprises of a comparator configured to compare the valid characterswith respect to a known ground truth value in order to identify one ormore candidate images and an image generator configured to generate afinal candidate image by combining the candidate images such that thecombination of the candidate images is dependent upon a predefinedcondition. The system further comprises of an output generating moduleconfigured to split the final candidate image into a predefined segmentsand recognizing a valid alphanumeric character associated with eachsegment therein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the system architecture in accordance with anembodiment of the invention.

FIG. 2 illustrates an exemplary flowchart in accordance with analternate embodiment of the invention.

FIGS. 3A and 3B illustrates the form of image after applyingmorphological closing in accordance with an alternate embodiment of thesystem.

FIGS. 4A, 4B, 4C, 4D, 4E, 4F and 4G illustrates comparative analyses ofthe binarization technique of the present invention with those of theprior arts in accordance with an embodiment of the invention.

DETAILED DESCRIPTION

Some embodiments of this invention, illustrating its features, will nowbe discussed:

The words “comprising”, “having”, “containing”, and “including”, andother forms thereof, are intended to be equivalent in meaning and beopen ended in that an item or items following any one of these words isnot meant to be an exhaustive listing of such item or items, or meant tobe limited to only the listed item or items.

It must also be noted that as used herein and in the appended claims,the singular forms “a”, “an”, and “the” include plural references unlessthe context clearly dictates otherwise. Although any systems, methods,apparatuses, and devices similar or equivalent to those described hereincan be used in the practice or testing of embodiments of the presentinvention, the preferred, systems and parts are now described. In thefollowing description for the purpose of explanation and understandingreference has been made to numerous embodiments for which the intent isnot to limit the scope of the invention.

One or more components of the invention are described as module for theunderstanding of the specification. For example, a module may includeself-contained component in a hardware circuit comprising of logicalgate, semiconductor device, integrated circuits or any other discretecomponent. The module may also be a part of any software programmeexecuted by any hardware entity for example processor. Theimplementation of module as a software programme may include a set oflogical instructions to be executed by the processor or any otherhardware entity. Further a module may be incorporated with the set ofinstructions or a programme by means of an interface.

The disclosed embodiments are merely exemplary of the invention, whichmay be embodied in various forms.

The present invention relates to a system and a method foridentification of alphanumeric characters present in a series in animage. In the very first step, two major peaks are identified from apattern of the scale image and a number of binarized images areobtained. The components which are unwanted are removed from thebinarized images. Further, one or more candidate images are segmentedsuch that each segment contains a valid character in order to generate afinal candidate image.

In accordance with an embodiment, referring to FIG. 1, the system (100)comprises of an image capturing device (102) adapted to capture theimage comprising the alphanumeric characters present in the series (asshown in step 202 of FIG. 2). The system further comprises of aprocessor (104) which is configured to produce a set of identifiablecharacters out of the series of alphanumeric characters (as shown instep 206 of FIG. 2). The processor further comprises of a computingmodule (106), a binarization module (108), a filter (110), a comparator(112) and an image generator (114).

In accordance with an embodiment, still referring to FIG. 1, the imagecapturing device captures the image in a gray scale. The image capturingdevice may include a camera. This camera may be coupled to some otherelectronic device. By way of specific example, the camera may be presentin a mobile phone. The images are captured by the image capturing device(102) in a plurality of frames. These images may again comprise of aseries of alphanumeric characters to be further identified and hence mayinclude one or more type of noise. The captured images are furtherprocessed by the processor. The processor (104) then produces a set ofidentifiable characters out of the series of alphanumeric characterspresent in the image.

By way of a specific example, the number of alphanumeric characterspresent in the series may include but is not limited to 17 alphanumericcharacters.

The processor (104) further comprises of a computing module (106) whichis configured to compute a pattern for recognizing a pixel intensitydistribution in the image for determining a background peak and aforeground peak. The pixel intensity is recognized in a form of ahistogram.

The computing module (106) enhances the quality of the input image byapplying the retinex strategy (as shown in step 204 of FIG. 2). Theimage enhancement is based on two main observations that there are twosources of noises. One is multiplicative in nature that appears due tothe background text and the reflection from the glass. The computingmodule (106) further converts the image into a gray scale image. A grayscale image is one in which the only colors are shades of gray. Anintensity histogram of the gray scale image is computed which is a graphshowing the number of pixels in an image at each different intensityvalue found in that image (as shown in step 208 of FIG. 2). By way ofspecific example, for an 8-bit grayscale image there are 256 differentpossible intensities, and so the histogram will graphically display 256numbers showing the distribution of pixels amongst those grayscalevalues. Further, from this intensity distribution two major peaks areidentified, one located near the value 0 and the other located near thevalue 255 (as shown in step 210 of FIG. 2). These peaks are representedas the background part and the foreground part of the imagerespectively.

The processor (104) further comprises of the binarization module (108)which is configured to generate a plurality of binarized images.

In accordance with an embodiment, the disclosed binarization method isbased on two main observations that there is a slight gray scalevariation between the background text (BGT) and the text of interest(TOI) and strictly 17 alphanumeric characters are present in thecaptured image. A specific number (n) of dynamic threshold values (pixelvalues) between the background peak and the foreground peak are used forbinarization (as shown in step 212 of FIG. 2). For an image in 8 bitsper pixel format, this number is 16 which are obtained heuristically.Thus, n numbers of binarized images are obtained from the single grayscale image (as shown in step 214 of FIG. 2).

In accordance with an embodiment, the foreground pixels of each suchimage is labeled using Connected-component labeling method.Connected-component labeling is an algorithmic application of graphtheory, where subsets of connected components are uniquely labeled basedon a given heuristic. A graph, containing vertices and connecting edges,is constructed from the input image. The vertices contain informationrequired by the comparison heuristic, while the edges indicate connected‘neighbors’. An algorithm traverses the graph, labeling the verticesbased on the connectivity and relative values of their neighbors.Following the labeling stage, the graph may be partitioned into subsets,after which the original information can be recovered and processed.

The processor (104) further comprises of a filter (110) which isconfigured to remove unwanted components from the n number of binarizedimages to identify one or more valid characters (as shown in step 216 ofFIG. 2). The components that are too small or too big are removed. Acomponent is defined to be too small if the number of pixels with thatparticular level is less than 100 or if the component has a height (h)or width (w) less than 3 pixels. Similarly, a component is defined to betoo big if the number of pixels with that particular level is more thanwidth/4 or if,

h>(ht_image/3) or

w>(wd_image/4)

where,

-   ht_image is the height of the image and wd_image is the width of the    image.

The processor (104) further comprises of a comparator (112) configuredto compare the valid characters with respect to a known ground truthvalue in order to identify one or more candidate images. The knownground truth value (k) is equal to the number of alphanumeric characterspresent in the series.

The comparator (112) is used to remove the unwanted components in orderto identify the candidate images. If the number of components is lessthan k/2, it means that actual k number of characters is either veryunder segmented or the binarized image doesn't include all validcharacters as foreground (as shown in step 218 of FIG. 2). So thisbinarized image is not considered as a candidate image. Similarly, ifthe number of components are greater than k*3 then on the average onevalid character is over segmented to more than 3 segments (as shown instep 218 of FIG. 2). The over-segmented and under segmented binarizedimages are disregarded. The remaining binarized images are considered asthe candidate images. Thus, only a few valid images are left out of nbinarized images. Typically, the number of such candidate images foreach input image is less than or equal to 3 (for a case where number ofalphanumeric characters present in the series are 17).

The processor (104) further comprises of an image generator (114) whichis configured to generate a final candidate image by combining thecandidate images (as shown in step 220 of FIG. 2). The candidate imagesare combined by marking the pixels as background text (BGT) only if itis decided as a background text in more than half of the candidateimages. On the fulfillment of this predefined condition, the finalcandidate image is constructed.

The system (100) further comprises of an output generating module (116)which is configured to split the final candidate image into a predefinedsegments such that each segment contains only one valid character. Thecandidate image is split into a number equal to the number ofalphanumeric characters present in the series (as shown in step 222 ofFIG. 2).

In accordance with an embodiment, a conventional method of skewcorrection is used prior to the segmentation. The following method ofsegmentation is based on the observation that the number of validcharacters is equal to the number of alphanumeric characters present inthe series (k). The steps involved in the character and numeralsegmentation and recognition method is as follows:

-   -   Identify the columns without any foreground pixel. If        consecutive such rows are attained, the middle of these columns        is taken as the candidate cut column (CCC). Let the number of        CCC obtained be n.    -   Find the distance (δ) between the consecutive CCCs. Let the        distance between the i^(th) and the (i+1)^(th) CCC be defined as        δ_(i)=|CCC_(i+1)−CCC_(i)|    -   Find the median (med_(δ)) of δ_(i)∀iε(1,2, . . . ,n) wherein is        the number of CCCs in the image. A heuristically obtained        tolerance factor τ is used to define the threshold (Th_(δ))        which is defined as Th_(δ)=(med_(δ)−τ).    -   If k−1 components are obtained which are nearly equally spaced        columns, each segment is used as a candidate segment.    -   If n>k−1 then it is concluded that some valid character is        horizontally over segmented. Subsequently such CCC's are merged        and n is reduced by one iteratively.    -   If n<k−1, then it is concluded that there is definitely some        valid characters touching each other.

Thus, k numbers of segments are obtained, each having a valid character.The segments obtained may be in over segmented form.

In accordance with an embodiment, referring to FIG. 3, if any suchsegment includes multiple component labels, they are merged by applyinga morphological closing (as shown in step 224 of FIG. 2). The FIG. 3( a)shows an over-segmented character and FIG. 3 b) shows the same characterafter applying morphological closing. Closing is similar to as ifopening is performed in reverse. It is defined simply as dilationfollowed by erosion using the same structuring element for bothoperations. The closing operator therefore requires two inputs: an imageto be closed and a structuring element. Gray level closing consistsstraightforwardly of a gray level dilation followed by gray levelerosion. Closing is the dual of opening, i.e. closing the foregroundpixels with a particular structuring element, is equivalent to closingthe background with the same element.

In accordance with an embodiment, FIG. 4( g) illustrates the improvedresults of binarization technique as disclosed in the present inventionwith respect to prior arts shown in FIGS. 4( a), (b), (c), (d), (e) and(f).

BEST MODE/EXAMPLE FOR WORKING OF THE INVENTION

The system and method illustrated for identification of alphanumericcharacters present in a series in an image may be illustrated by workingexample stated in the following paragraph; the process is not restrictedto the said example only:

Let us consider an image of a vehicle identification number (VIN)captured by a person through his camera of resolution 2 mega pixel inhis mobile. Let us consider the image is affected by plurality of noise(mud on number plate, shadow of some other vehicle etc). Let originallythe number is MH05 1424 66720087 (17 numbers including 2 alphabets). Outwhich the number and/or alphabets which are clear include M-0514-4---2008- (rest of the numbers are partially identifiable). Thisimage comprising numbers and alphabets before being identified by OCR(Optical character recognition) is enhanced by the above describedmethod and system. The histogram is first computed giving the peakpoints of background and foreground. This gives a threshold value (say16) by which a plurality of binarized images are obtained.

These binarized images are further filtered and unwanted are removed andcertain valid characters are obtained. These valid characters are usedfor identifying candidate images by comparing it to a ground truth value(which in our case is 17). Now, a final candidate image is created bycombining these small candidate images and thus missing characters areidentified.

This process is repeated for identification of all the missing or noiseaffected characters and finally the quality of image is enhanced beforebeing processed by the OCR.

We claim:
 1. A method for identification of alphanumeric characterspresent in a series in an image, the method comprising processorimplemented steps of: capturing the image comprising the series ofalphanumeric characters; processing the image for producing a set ofidentifiable characters out of the series of alphanumeric characters;the processing further comprising: computing a pattern for recognizing apixel intensity distribution in the image for determining a backgroundpeak and a foreground peak; generating a plurality of binarized imagesby selecting a plurality of dynamic threshold values between thebackground peak and the foreground peak; filtering the generatedbinarized images by removing unwanted components from plurality ofimages to identify one or more valid characters; identifying one or morecandidate images by comparing the valid characters with respect to aknown ground truth value; generating a final candidate image bycombining the candidate images such that the combination of thecandidate images is dependent upon a predefined condition; and splittingthe final candidate image into a predefined segments and recognizing avalid alphanumeric character associated with each segment therein. 2.The method as claimed in claim 1, wherein the series of alphanumericcharacters includes but is not limited to 17 alphanumeric characters. 3.The method as claimed in claim 1, wherein the pattern for recognizingthe pixel intensity is in a form of histogram.
 4. The method as claimedin claim 1, wherein the plurality of pixel values depends upon thenumber of alphanumeric characters present in the series.
 5. The methodas claimed in claim 1, wherein the unwanted components comprises of toobig and too small components, such that a component is too small if thenumber of pixels with that particular level is less than 100 and acomponent is too big if the number of pixels with that particular levelis width/4.
 6. The method as claimed in claim 1, wherein the one or morecandidate images are identified by disregarding over-segmented andunder-segmented binarized images.
 7. The method as claimed in claim 1,wherein the ground truth value is equal to the number of alphanumericcharacters present in the series.
 8. The method as claimed in claim 1,wherein the predefined condition for combining the candidate imagesrefers to marking the pixels of all the candidate images as backgroundif more than half of the candidate images are background.
 9. The methodas claimed in claim 1, wherein the candidate image is split into anumber equal to the number of alphanumeric characters present in theseries.
 10. The method as claimed in claim 1, wherein the multiplecomponent labels in the valid alphanumeric character are merged byapplying morphological closing.
 11. A system for identification ofalphanumeric characters present in a series in an image, the systemcomprising: an image capturing device for capturing the image comprisingthe alphanumeric characters present in the series; a processorconfigured to produce a set of identifiable characters out of the seriesof alphanumeric characters; the processor further comprising: acomputing module configured to compute a pattern for recognizing a pixelintensity distribution in the image for determining a background peakand a foreground peak; a binarization module configured to generate aplurality of binarized images by selecting a plurality of dynamicthreshold values between the background peak and the foreground peak; afilter configured to remove unwanted components from the plurality ofimages to identify one or more valid characters; a comparator configuredto compare the valid characters with respect to a known ground truthvalue in order to identify one or more candidate images; an imagegenerator configured to generate a final candidate image by combiningthe candidate images such that the combination of the candidate imagesis dependent upon a predefined condition; and an output generatingmodule configured to split the final candidate image into a predefinedsegments and recognizing a valid alphanumeric character associated witheach segment therein.
 12. The system as claimed in claim 11, wherein theimage capturing device includes a camera.
 13. The system as claimed inclaim 11, wherein the computing module computes a histogram pattern forrecognizing the pixel intensity.
 14. The system as claimed in claim 11,wherein the captured image includes 17 alphanumeric characters presentin the series.